The state of health estimation of lithium-ion batteries based on data-driven and model fusion method

Huang, Peng; Gu, Pingwei; Kang, Yongzhe; Zhang, Ying; Duan, Bin; Zhang, Chenghui

doi:10.1016/j.jclepro.2022.132742

Cited by 34 publications

(3 citation statements)

References 48 publications

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“…Domestic and international scholars have proposed various methods for estimating the SOH of lithium-ion batteries, which can generally be divided into two categories: model-based methods [11][12][13][14][15][16][17][18] and data-driven methods [19][20][21][22][23][24][25][26]. Model-based methods involve establishing appropriate equivalent models of lithium-ion batteries to simulate average value, and standard deviation, are extracted.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Zhou,

Zhang,

Zhang

et al. 2024

WEVJ

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Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%.

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Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Zhou,

Zhang,

Zhang

et al. 2024

WEVJ

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show abstract

“…Reference [13] established a model combining incremental capacity analysis and bidirectional long-and short-term memory neural networks based on health characteristic parameters to predict the SOH of lithium-ion batteries. Reference [14] put forward a data-driven and an improved equivalent circuit model fusion method for SOH estimation based on constant voltage charging process.…”

mentioning

confidence: 99%

Life cycle capacity evaluation for battery energy storage systems

Sun,

Yue,

Zhuang

et al. 2024

Electr Eng

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“…Therefore SOH estimation, as one of the core indicators of state estimation, is essential to ensure both the usage cost and the safe operation of EVs. 10,11 In general, the SOH of the Li-ion battery is defined as follows: 12…”

mentioning

confidence: 99%

State-of-Health Prediction for Lithium-Ion Batteries based on Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Neural Network Optimized by Slime Mould Algorithm

Sun,

Zhang

2023

J. Electrochem. Soc.

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State-of-health prediction of lithium-ion batteries has been one of the popular research subjects in recent years. Accurate state-of-health prediction has an especially significant role for battery management systems. This study combines the empirical mode decomposition and bidirectional gated recurrent unit neural network optimized by slime mould optimization algorithm to develop the state-of-health prediction model. First, to deal with the short-term capacity regeneration characteristics and the long-term degradation trend in state-of-health curve, the original battery state-of-health sequence is decomposed into some intrinsic mode functions and one residual sequence by using the empirical mode decomposition. Then, slime mould algorithm is used to automatically find the best hyperparameters of the bidirectional gated recurrent unit model. Finally, the bidirectional gated recurrent unit model is established to predict the state-of-health of lithium-ion batteries. The experimental results show that the proposed state-of-health prediction method always exhibit great accuracy both for the LiCoO2 battery datasets from the Center for Advanced Life Cycle Engineering and for the LiNCM battery datasets in our own laboratory. Furthermore, for the same type of batteries, the offline established prediction model does not need to be retrained. All these indicate that this combined model has high robustness, excellent universality, and superb practicality.

show abstract

The state of health estimation of lithium-ion batteries based on data-driven and model fusion method

Cited by 34 publications

References 48 publications

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA

Life cycle capacity evaluation for battery energy storage systems

State-of-Health Prediction for Lithium-Ion Batteries based on Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Neural Network Optimized by Slime Mould Algorithm

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